Statistical Analysis Plan Template A Statistical Analysis Plan (SAP) is a detailed document that outlines the statistical methods that will be used to analyse data in a clinical trial or other research studies. The SAP should be developed and implemented in consultation with a biostatistician or other statistical expert to ensure that the analysis is appropriate and rigorous. This template has been created to assist researchers with their Statistical Analysis Plan. We recommend the following: The language used throughout the form should be easily understandable. Use local and simplified terms rather than scientific terminology and abbreviations. We recommend that you refer to the Plain English Campaign. [These guideline notes should be deleted or replaced with actual content] DISCLAIMER: This templates and the information available on this document are intended for reference purposes only. Although we make efforts to ensure the information is current and accurate, we do not provide any guarantees or warranties, either expressed or implied, regarding the completeness, accuracy, reliability, suitability, or availability of the website or the information, articles, templates, or graphics found within it. Any reliance you place on such information is entirely at your own discretion and risk.

[Add Project Logo here]

[Add Project Name here]

Statistical Analysis Plan

Trial Registration Number: [insert number]

Protocol Version: [associated with this document]

[Principal Investigator’s name]

Principal Investigator

[Email]

[Study Statistician’s name]

Study Statistician

[Email]

Study Coordinating Centre

[Add postal address]

[Add street address]

Signature Page

I give my approval for the attached SAP for the study entitled [STUDY TITLE] dated [SAP VERSION DATE].

Author of Statistical Analysis Plan:

Senior Statistician Responsible for Study:

Chief Investigator/Clinical Lead:

Revision history

Version	Date	Revision description
[Write version number]	[Date of revision]	[Initial version or description of the revisions made]

Statement of purpose

This Statistical Analysis Plan (SAP) details the planned statistical analyses for the [insert trial name] data. It functions independently from the protocol yet aligns with the protocol's key analytical points. Although revisions to the SAP are expected, changes aligning with the protocol's spirit would not necessitate an ethics committee review.

We assume the study documentation, provided by Data Management, complies with standard operating procedures. These procedures guide the creation and maintenance of study documentation, decision-making related to data handling, data capture methods, and the use of algorithms for data accuracy and preservation. Additionally, the SAP will offer a blueprint of the proposed tables/figures for presentation.

Scope

This SAP specifically covers the main analyses stipulated in the protocol. For inquiries not outlined in this SAP or those requiring further exploratory analyses, please refer to other distinct SAPs.

To adhere to the Biostatistics report's deadline, the needed analyses must be predetermined in this plan. Post sign-off, requests for extra analyses should be kept to a minimum. This allows for all preparatory programming to occur before the database is locked.

[Add other relevant statements specific to this study if necessary]

Introduction

Background and Rationale

This section lays out the context and rationale driving this trial, concisely presenting the research question and why we have decided to proceed with the trial.

Trial Background

The origins of this trial can be traced back to [provide a succinct summary of the motivations, past studies, identified gaps, or healthcare needs leading to this study].

Research Question

The trial seeks to answer the following primary research question: [state the research question, be it a specific hypothesis or a wider field-related inquiry].

Rationale for the Trial

The reasoning behind this trial involves: [insert additional/delete rationale for the trial below]

Necessity: [Explain why this research is currently crucial, considering its pertinence to ongoing medical issues, potential influence, or specific patient needs it could fulfil].

Innovation: [If applicable, highlight how this trial introduces novel elements to the field, like groundbreaking methodologies, treatments, or a unique study group].

Potential Impact: [Discuss potential results of the study and how they might improve the field, enhance patient care, or initiate further research].

Note: These reasons underline the need and benefits of this trial.

Study Objectives

The goals of this study are categorised into primary and secondary aims, clarifying our research direction, and intended outcomes.

Overall Aim

The study intends to [state the broad goal or goals of the study, like exploring a certain disease or testing a specific treatment].

Specific Research Objectives

Based on our main aim, the specific objectives are:

[List the detailed objectives that break down the main aim into measurable segments. These should come directly from the approved study protocol.

Primary Objective(s)

The primary objective(s) of this study is/are:

[Clearly state the main objective(s), which are typically focused on gauging the effectiveness of a treatment or intervention.]

Secondary Objective(s)

Alongside our primary objective(s), the secondary objective(s) include(s):

[List the secondary objectives. These are additional outcomes that can offer valuable data even if not the primary study focus.]

Study Endpoint(s)

We will use these endpoints to evaluate our objectives and the study's success:

Primary Endpoints

[List primary endpoints and their definitions. These are the main metrics used to ascertain if the primary objectives have been achieved.]

Secondary Endpoints

[List secondary endpoints and their definitions. These additional metrics will assess the secondary objectives.]

Exploratory Endpoints

[If applicable, list exploratory endpoints and their definitions. These are additional potential outcomes that might provide insightful findings.]

Note: These endpoint definitions are in line with the final approved study protocol.

Study outcomes

Study Design

This section gives a comprehensive overview of how the study is organised, which includes the trial type, the participant allocation, the interventions used, and other relevant elements.

Type of Trial and Allocation Ratio

This study follows a [type of trial] design. Participants are divided among different study groups according to the [allocation ratio].

Description of Interventions

The study encompasses interventions such as [outline the interventions, including what they are, their method of administration, and the individuals who will administer them].

Further details of the study design are as follows:

Study Location

The trial takes place at [provide location(s), including the institution, city, and country if necessary].

Randomisation

If relevant to the trial design, randomisation is accomplished via [name the software or method], leading to [number of randomised groups] distinct groups for comparison.

Type of Control(s)

The study employs control measures such as [type of controls].

Level of Blinding

The study uses a [level of blinding] blinding scheme to ensure unbiased results.

Frequency and Timing of Intervention

Interventions are administered [describe the frequency and timing of the interventions].

Duration of Study

The expected duration of the study is [length of the study from start to finish].

Note: By integrating these elements, our study design will ensure robust and reliable data collection, contributing to the validity and reliability of our results.

Randomisation

This section encompasses the specifics of randomisation within our trial, including the presence of any minimisation or stratification processes and the factors used in those processes.

Randomisation Process

Randomisation in our trial is achieved by [describe the method of randomisation].

Minimisation

In this study, minimisation is [indicate if used, and if so, describe the process].

Stratification

In this study, stratification is [indicate if incorporated, and if so, describe how it was conducted].

Stratifying Factors

Stratifying factors in this study comprise [list factors or specify where they can be found].

Note: By employing these randomisation techniques, our study aims to ensure a balanced and fair allocation of participants to treatment groups, thereby reducing bias and enhancing the credibility of the trial results.

Sample Size

This section discusses the determination of our study's sample size, providing an in-depth calculation or referencing the approved protocol.

Sample Size Calculation

Sample size calculation is based on [outline the method/formula] or is detailed in the approved study protocol [specify the location].

The final calculated sample size for our study is [state the total sample size]. This number has been determined to ensure sufficient power to detect a clinically significant difference, while also considering practical considerations such as the expected recruitment rate and potential for participant drop-out.

Note: By clearly outlining our sample size calculation, we aim to ensure transparency in our study design and enhance the reproducibility of our study findings.

Framework

This section outlines the statistical framework employed in our study, which could encompass superiority, equivalence, or noninferiority hypothesis testing. It also includes details on the comparisons that will be made within this framework.

Hypothesis Testing Framework

Our study employs a [state whether it is a superiority, equivalence, or noninferiority] hypothesis testing framework. This means that:

A superiority framework would suggest that we aim to prove that one treatment is better than another.

An equivalence framework would denote that we aim to prove that two treatments are equivalent within a specified margin.

A noninferiority framework implies that we aim to show that a new treatment is not worse than the standard treatment within a pre-specified margin.

Comparisons

The comparisons to be made within this framework are:

[List the specific comparisons that will be made. This could include comparing different treatment groups, comparing results at different time points, or comparing different types of outcomes.]

Note: By defining our hypothesis testing framework and detailing the comparisons we will make, we ensure our analysis remains focused and can adequately answer our research questions.

Statistical Interim Analysis and Stopping Guidance

This section presents details on interim analyses and potential early termination of the trial.

Interim Analyses

Interim analyses will be conducted at the following time points during the trial:

[Specify when the interim analyses will occur. These could be at certain points in time, after a specific number of participants have enrolled, or after the occurrence of a particular event.]

Each interim analysis aims to [describe the purpose of each interim analysis i.e., monitoring safety, checking validity of the study design, making a preliminary assessment of the interventions effectiveness etc].

Significance Level Adjustment

Considering these interim analyses, there will be [describe whether there will be any adjustments to the significance level. If so, explain the method for adjustment and why it is necessary. This could be to account for multiple comparisons or to maintain the study power].

Stopping Guidelines

The trial may be prematurely ended under the following circumstances:

[Outline the criteria that would lead to early termination of the study. These could include safety concerns, evidence of effectiveness from the interim analyses, or operational issues like recruitment difficulties.]

Note: These guidelines for interim analysis and early termination of the study are designed to make sure the safety of the participants, the integrity of the data, and the effectiveness of the study protocol.

Statistical Principles

Confidence Intervals and P values

In this section, we define the statistical significance level adopted in our study, explain any multiplicity adjustments, and specify the confidence intervals to be reported.

Level of Statistical Significance

Our study's statistical tests will be [indicate whether tests will be one-tailed or two-tailed], with a consistent [insert the chosen significance level, commonly 0.05 or 5%] significance level throughout the analyses.

Multiplicity Adjustments

Our study will [state whether adjustments for multiplicity will be made]. If so, the reasoning for these adjustments is [clarify why the adjustments are needed; this could be due to the number of hypotheses tested, the number of comparisons made, etc.]. We will control the Type I error using [detail the method or approach to control the Type I error rate].

Handling of Missing Data

In our study, missing data for primary and secondary endpoints will be managed by [outline the strategies for handling missing data; these could be multiple imputation, last observation carried forward, or others].

Presentation of Data

We will present continuous variable summaries as means and standard deviations if the data is normally distributed, or as medians and inter-quartile ranges if the data is skewed. Meanwhile, categorical variables will be displayed as frequencies and percentages.

Statistical Tests

We will select statistical tests based on the data type. For continuous variables, we will use t-tests or Mann-Whitney tests, and for categorical data, we will employ Chi-squared tests or similar, as appropriate.

Analyses and Reporting

Our study statistician will perform all analyses. Alongside this Statistical Analysis Plan (SAP), we will provide a separate Excel file containing SAP tables specific to this study, which details all the analyses stated below. Find more about this file in Appendix I.

Note: This methodology ensures our statistical analyses are robust, transparent, and account effectively for potential sources of error.

Adherence and Protocol Deviations

This section outlines the definition of adherence to the intervention, how it is assessed and presented, as well as the definition and summary of protocol deviations in the trial.

Adherence to the Intervention

Adherence in our study refers to [define what constitutes adherence to the intervention. This might include frequency of use, dosage taken, or attendance at appointments, among others.]

Assessment of adherence will be done via [describe how you plan to measure adherence. This could include methods such as patient diaries, pill counts, biomarkers, or electronic monitoring devices.]

Presentation of Adherence

The display of intervention adherence will take the following form:

[Detail how adherence will be presented. This could be averages, ranges, percentages of participants demonstrating adherence, or visual representation such as graphs].

Protocol Deviations

Protocol deviations in this trial are classified as [define what comprises a protocol deviation. This could be failing to adhere to the intervention, missing scheduled appointments, or non-compliance with the prescribed study procedures].

Summary of Protocol Deviations

The following types of protocol deviations will be summarised: [enumerate the types of protocol deviations to be summarised. These could include all deviations, only major deviations, or deviations impacting specific outcomes].

Note: By clearly outlining and reporting on adherence and protocol deviations, we ensure that our study results accurately reflect the effects of the intervention, while also considering the real-world application of the treatment.

Analysis Populations

This part of the document delineates the populations utilised for analysis in our study, encompassing the intention-to-treat, per-protocol, complete-case, and safety populations.

Intention-to-Treat Population

The intention-to-treat (ITT) population comprises all participants randomised into the study, irrespective of their compliance with the study protocol, discontinuation from the study, or reception of the intended treatment. The ITT approach renders a pragmatic perspective of treatment effectiveness while preserving the advantages of randomisation.

Per-Protocol Population

The per-protocol (PP) population includes participants with substantial compliance to the study protocol. Participants with significant protocol deviations such as non-adherence to the intervention or missed key assessments are excluded. The PP analysis provides an estimate of the treatment effect under optimal conditions.

Complete-Case Population

The complete-case population includes participants who have no missing data on the primary outcome measure and have completed the study. This analysis method yields an unbiased estimate of the treatment effect, assuming data are missing completely at random.

Safety Population

The safety population consists of participants who have received at least one dose of the study intervention. Analyses carried out on this population aim to evaluate the safety and tolerability of the intervention.

Note: By conducting analyses on these distinct populations, we aim for a comprehensive understanding of the treatment effect, considering various scenarios that may occur during a clinical trial.

Trial Population

This section gives details on the trial population, including screening data, eligibility, and recruitment.

Screening Data

If collected, screening data will be reported to illustrate the representativeness of the trial sample. This involves a description of [Specify what aspects will be described. This might include demographic characteristics, medical history, baseline characteristics, etc.].

Eligibility

The eligibility criteria for this study are summarised as follows:

Inclusion criteria:

[List all inclusion criteria consistent with the protocol. This typically includes criteria related to age, gender, diagnosis, disease stage, etc.]

Exclusion criteria:

[List all exclusion criteria consistent with the protocol. This typically includes criteria related to specific medical conditions, treatments, or other factors that would disqualify a participant from the study.]

Recruitment

Recruitment will be outlined as follows:

[Here, describe methods to attract participants, places for recruitment, timeline for recruitment etc.]

Note: By providing clear and detailed information about our trial population, we aim to ensure transparency and reproducibility, contributing to the overall quality and validity of our study.

Variable Definitions

The following definitions relate more specifically to the derived variables and non-standard definitions required for analysis of the data that are specific to the analyses referred to throughout this SAP. Please refer to the study protocol for a complete list of abbreviations set out for this study.

[List all variable definitions and a description of the type of variable (Likert, continuous, binary, etc.) with a description if provided]

CONSORT Statement

[Only applicable for randomised control trials, if not applicable, please disregard this section] In accordance with good research practice, we will ensure that all individuals invited to partake in this study are properly accounted for by preparing a CONSORT (Consolidated Standards of Reporting Trials) statement.

The CONSORT statement aids in delivering transparency and exhaustive reporting of the trial. It ensures the accurate and transparent documentation of the test design, execution, analysis, and interpretation. "In this context, we will explore the reasons for non-participation with respect to the external validity of the study, considering potential sources of bias due to protocol breaches.

All reasons leading to early withdrawal from the study, or the intervention will be thoroughly documented and listed for each participant that prematurely discontinued.

Furthermore, the number of participants that were registered but not randomised will be accurately reported. The CONSORT statement aids in providing a detailed account of the flow of participants throughout the study, enhancing the transparency and reliability of the trial's execution and results.

Withdrawal/Follow-Up

This section outlines how participant withdrawal and follow-up will be managed in the study.

Extent of Withdrawal

We will differentiate between those participants who have withdrawn from the intervention only, and those who have disengaged from both the intervention and the follow-up.

Timing of Withdrawal/Missed Follow-Up

The precise timing of withdrawal or loss to follow-up will be recorded. This will provide insights into the participant's experience with the intervention and potential withdrawal reasons.

Reasons for Withdrawal/Lost to Follow-Up

We will diligently collect and summarise the reasons for withdrawal or loss to follow-up, which could include personal reasons, adverse events, or protocol violations, among others.

Presentation of Withdrawal/Lost to Follow-Up Data

The study results will clearly present information about participant withdrawal or loss to follow-up, including the number and timing of withdrawals, reasons for withdrawal, and the associated demographic information.

Note: By detailing these aspects, we aim to maintain transparency and enhance the understanding of the study's overall findings.

Baseline Patient Characteristics

This section summarises the baseline characteristics of the participants. It also outlines how these summaries will take place.

List of Baseline Characteristics

The study will summarise the following baseline characteristics:

Age

Sex

[Other relevant characteristics such as race, ethnicity, body mass index, medical history, current medications, vital signs, laboratory parameters, etc.]

Summarisation of Baseline Characteristics

Baseline characteristics will be summarised in a descriptive manner. Continuous variables like age and BMI for example, will be represented with measures of central tendency (median or mean) as well as variability (interquartile range or standard deviation). In addition, categorical variables like sex and race will be shown as the number and percentage of participants in each category.

Note: By summarising baseline characteristics, a more thorough overview of the study population will take place, leading to a more detailed understanding of the study's outcomes.

Analysis

This section defines the primary and secondary outcomes of our study, along with the methods for their collection and any necessary calculations.

Outcome Definition

The outcomes of this study will be outlined as follows:

Primary Outcome(s):

[List and describe each primary outcome]

Specification and Timing: [Specify each primary outcome and when it will be measured]

Collection Method: [Describe how the outcome data will be collected, such as the method and instruments used]

Measurement and Units: [Specify the exact measurements and units used, such as glucose control measured in mmol/mol or %]

Calculation/Transformation: [Describe any calculation/transformation, if applicable, which is used to obtain the outcome, such as change from baseline, time to event, quality of life (QoL) score, logarithm, etc.]

Secondary Outcome(s):

[List and describe each secondary outcome]

Specification and Timing: [Specify each secondary outcome and when it will be measured]

Collection Method: [Describe how the outcome data will be collected, such as the method and instruments used]

Measurement and Units: [Specify the exact measurements and units used, such as blood pressure measured in mmHg]

Calculation or Transformation: [If applicable, describe any calculation or transformation used to derive the outcome, such as change from baseline, disease severity score, time to event, logarithm, etc.]

In addition to the above, the primary or key secondary endpoints order of importance (i.e., the order that they will be tested) will be included, if applicable.

Note: Through a comprehensive analysis and the precise definition of our primary and secondary outcomes, we endeavour to answer the research questions with the highest degree of accuracy and validity.

Analysis Methods

This section details the analysis methods that will be utilised in our study and how the treatment effects will be presented.

Primary Outcome(s) Analysis

Our primary outcome(s) will be analysed using [specify the specific statistical methods e.g., t-test, chi-square test, logistic regression, Cox proportional hazards model, etc.].

We will estimate and present treatment effects as [state the format, such as risk ratios, odds ratios, hazard ratios, mean difference, etc.], including their [state %] confidence intervals.

Adjustment for Covariates

Adjustment for potential confounding variables like [list potential covariates, such as age, sex, baseline disease severity, comorbidities, etc.] will be performed if they could influence the relationship between the treatment and the primary outcome(s).

Assumption Checking

To guarantee the validity of the statistical models used, we will perform several checks for assumption. These include but are not limited to:

Normality: For continuous variables, the normality assumption will be verified using methods such as the visual inspection of histograms and Q-Q plots, as well as statistical tests e.g., the Shapiro-Wilk test or the Kolmogorov-Smirnov test.

Proportional hazards: For time-to-event data, we will verify the assumption of proportional hazards using statistical tests such as Schoenfeld residuals test, as well as graphical methods such as log-log plots.

Linearity: For regression models, we will visually inspect scatterplots and use statistical tests to check assumption of linearity between the predictor variables and the outcome.

Homoscedasticity: For regression models, we will verify the assumption of constant variance of the errors (homoscedasticity) using methods such as visual inspection of residual plots and statistical tests like the Breusch-Pagan test or White's test.

Alternative Methods

If the necessary assumptions do not hold, alternative statistical methods such as [non-parametric methods (e.g., Wilcoxon rank-sum test or Kruskal-Wallis test) or time-dependent covariates or stratified Cox models] will be used.

Sensitivity Analyses

We will conduct sensitivity analyses for each outcome to evaluate the robustness of the results. This may involve:

[Using different statistical models or methods]

[Excluding outliers or influential data points]

[Varying the handling of missing data (e.g., complete-case analysis, multiple imputation)]

[Changing the definition or categorisation of variables]

[Insert other relevant analyses]

Subgroup Analyses

We will conduct subgroup analyses for selected endpoints in our study, which include [list all endpoints here which will have subgroup analyses]. Subgroups will be defined by variables such as [list variables to define subgroups here and its levels].

Additional analysis considerations involving subgroups, such as forest plots or other visualisation techniques, will be addressed as follows:

[Mention other analysis issues involving subgroups here if any, including forest plot etc]

Note: This section should be removed if there are no planned subgroup analyses for the study. Furthermore, delete 'it's interaction with intervention' if there is only one intervention group).

Data

Sources

Data for our study will come from various platforms: [List all data sources like Redcap data, Oracle data, digital data, Excel data, etc.].

Variable Definitions

This section outlines the definitions for certain derived variables and non-standard terms that are specific to this study. The variable definitions are:

[List all variable definitions and describe the type of variable, such as Likert, continuous, binary, etc.]

Quality Control Procedures

Several quality control procedures will be employed to ensure our data's integrity. These include:

Data Verification and Validation: [Outline the procedure for verifying and validating data]

Error Checking: [Describe how data errors will be detected and corrected]

Handling Outliers: [Describe how outliers will be identified and managed]

Management of Protocol Violations: [Describe how protocol violations will be handled]

Missing Data

Missing data will be addressed as follows:

Reporting: [Describe how missing data will be reported]

Assumptions/Statistical Methods: [Outline the statistical methods or assumptions used to deal with missing data, such as multiple imputation]

Additional Analyses

In addition to the main analyses described earlier, this study will carry out additional statistical analyses as detailed below.

Complier-Average Causal Effect Analysis: [Explain the need and method for carrying out this analysis]

Harms

In this section, the methods for summarising safety data, like details about severity, expectedness, and causality of adverse events during the study, will be outlined.

Severity, Expectedness, and Causality: [Clarify the process for deciding the severity, expectedness, and causality of adverse events]

Coding or Categorising Adverse Events: [Outline how adverse events will be coded or categorised]

Adverse Event Data Analysis: [Describe how adverse event data will be analysed e.g., only grade 3/4 events, incidence case analysis, or intervention emergent analysis]

Safety Evaluation

Safety evaluation is crucial to comprehend the potential risks linked with the intervention compared to the control. Data relevant to this will be thoroughly documented and reported.

Intervention Tolerance: All adverse event data accumulated on the [form X] will be disclosed to assess the safety and tolerance of the intervention.

Safety Data: Safety data will be fully presented via frequency distributions, descriptive statistics, and tabulations. This includes serious and non-serious adverse events for the duration of the trial.

Coding of Medication and/or Adverse Events

This section is relevant if medication and/or adverse event data are collected and require coding.

Medication Coding System: [If applicable, specify the system used for coding medication]

Adverse Events Coding: [If adverse events are coded, specify the classification system in use, for example, ICD9, ICD10, or MedDRA]

Adverse Events Reporting: [Outline the contents included in the adverse events report and state the frequency of presenting adverse events reporting]

Incidence of Adverse Events (AEs)

The occurrence of all adverse events will be systematically summarised, as displayed in ANNEX ##. Additionally, incidence rate ratios comparing the intervention and the control intervention will be computed and presented.

The total number of AEs, the number of participants experiencing at least one AE, and the number of patients under each organ class and preferred term will be recorded in the ANNEX ##.

For the summary involving the number of patients, multiple events per patient will be handled as follows: Patients may report more than one AE per system organ class and preferred term. In every level of patient summarisation, a patient will be counted once if they report one or more events.

Relationship of Adverse Events to Study Intervention

[Provide details about the process or methods for determining the relationship of adverse events to the study intervention]

Severity of Adverse Events (AEs)

Adverse events will be classified by their severity.

[List the severity levels here]

[List the severity levels here]

[List the severity levels here]

If the severity of an AE is not reported, the severity will be categorised as "missing".

Serious Adverse Events

Adverse events that result in treatment discontinuation or study termination will be individually listed.

Adverse Events Leading to Treatment Discontinuation/Study Termination

Adverse events that result in treatment discontinuation or study termination will be individually listed.

Fatal Adverse Events

Any adverse events leading to death will be separately listed for further analysis.

Statistical Software

For all statistical computations and analyses, we will utilise:

Software Package 1: [Describe briefly why this software is being used, and the types of analyses it will be used for.]

Software Package 2: [Describe briefly why this software is being used, and the types of analyses it will be used for.]

This comprehensive software suite ensures that our statistical procedures are rigorous, reliable, and reproducible. Additionally, the usage of these specific packages allows us to provide clear and comprehensible reports that effectively communicate our findings. Please note that all the versions of the software and any relevant plugins or extensions will be recorded to ensure analysis reproducibility.

Dissemination

Upon completion of the study, post data lock, we will undertake all statistical analyses as outlined in the study protocol. We aim to produce a comprehensive statistical analysis report, providing a summary of the principal findings of the study. Detailed results will be appended as ANNEX tables.

The Biostatistics (BS) team will fully own this report, furnished in a PDF format, along with the attached read-only tables. Any alterations to the interpretations, conclusions, or tables/figures beyond what is presented in the statistical analysis report, will not be the responsibility of the BS team.

It is imperative to note that the primary purpose of the statistical report is to display the results and aid the client, whether internal or external, in interpreting the findings. This report is not intended for broader dissemination. Should there be any intention to use information from the statistical analysis report for publications or similar purposes, it is necessary to notify the BS team in advance.

Changes and Amendments

This section documents any alterations to the Protocol and/or SAP.

If there are deviations from the statistical approach outlined in the protocol, as stated in the statistical analysis plan, those changes will be summarised here.

While analyses are consistent with the protocol specifications, there may be instances where different or supplementary analyses become necessary. Any such modifications will be explained and justified in this section. Identifying analyses that deviate from the protocol significantly enhances clarity and understanding of the changes made. Furthermore, important non-statistical changes to the protocol will also be noted in this section. These may include elements such as the addition of a new treatment group.

In such cases, clear justification and detailed explanation for these changes will be provided to ensure transparency and comprehensibility.

References

This section encompasses the acknowledgement of references pertinent to the statistical theory or precedent for the specific type of analysis undertaken in this study, as applicable.

If nonstandard statistical methods are used in the analyses, relevant references providing background or detailed explanations of these methods will be provided. This is to ensure that the statistical methodologies implemented are transparent and understandable.

A reference will be made to the Data Management Plan. This plan serves as a blueprint for the handling and management of data throughout this research, maintaining its quality and integrity.

The Trial Master File (TMF) and Statistical Master File (SMF) will be referred to, as they contain the essential documents which permit evaluation of the conduct of the trial and the quality of the data produced. These files collectively provide a comprehensive record of the trial's procedures and events.

References will also be made to other applicable standard operating procedures or documents to be adhered to during the study. These procedures provide a systematic and consistent approach to certain study tasks and ensure that they are carried out efficiently and effectively.

All references are important as they provide supportive evidence for the methods employed, ensuring transparency and reliability in the study procedures and analyses.

Appendix I: List of ANNEX Table templates and Figures

More detailed instruction. [List of tables and figures here. Add and remove rows, columns and/or images as needed.]

Consort flowchart [if applicable.]

2x2 Contingency table

[Variable 1 name]	[Variable 2 name]	Total n (%)	[Statistical test]
[Outcome A] n (%)	[Outcome B] n (%)
[Exposure A]
[Exposure B]
Total

Multiple variable contingency table

[Variable 1 name]	[Variable 2 name]	[Variable 3 name]	Total n (%)	[Statistical test]
[Outcome A] n (%)	[Outcome B] n (%)	[Outcome C] n (%)	[Outcome A] n (%)	[Outcome B] n (%)	[Outcome C] n (%)
[Exposure A]
[Exposure B]
Total